Extended Abstract Content Based Medical Image Retrieval (CBMIR) has reached a very challenging threshold, related to the gap between low-level medical image features and the semantic highly specialized medical information and knowledge; to the important context-dependence of the query and navigation; and the wide distribution of the medical data and knowledge. Answers to questions concerning se...

Images provide a powerful means to represent data, and many applications have as fundamental components the acquisition, processing and storing of huge amount of images. A typical example of such application is medical imaging. In hospitals working with medical images, large amounts of image data are received daily for processing, analysis and archiving. This arises the necessity of constructin...

Due to the vast number of medical technologies and equipments the medical images are growing at a rapid rate. This directs to retrieve efficient medical images based on visual contents. This paper proposed the content based medical image retrieval system by means of Gabor wavelet to extract texture features of MRI scan images. Then the k-means clustering, progressive retrieval strategy and Eucl...

In medical images, Content-based image retrieval (CBIR) is a primary technique for computer-aided diagnosis. Many research works were developed in content based medical image retrieval, but the techniques have the drawback of low efficiency and high computation cost. To avoid such negative aspects a new enhanced Content Based Medical Image Retrieval (CBMIR) based on MFCM clustering technique is...

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vec...

We present the methods we applied in the four different tasks of the ImageCLEF 2007 content-based image retrieval evaluation. We participated in all four tasks using a variety of methods. Global and local image descriptors are applied using nearest neighbour search for the medical and photo retrieval tasks and discriminative models for the object retrieval and the medical automatic annotation t...

Medical imaging is a precious and essential tool in healthcare systems, helps the physicians to emanate good quality of treatment. The advancement in medical Technology has resulted in a huge number of medical images which are stored in a database for future purpose. It is very imperative to build an effective retrieval system which browse through entire database in diagnosing the various disea...

MedGIFT is a medical imaging research group of the Geneva University Hospitals and the University of Geneva, Switzerland. Since 2004, the medGIFT group has participated in the ImageCLEF benchmark each year, focusing mainly on the medical imaging tasks. For the medical image retrieval task, two existing retrieval engines were used: the GNU Image Finding Tool (GIFT) as image retrieval engine and ...

Content-based image retrieval is one of the techniques of image mining. Content-based image retrieval system (CBIR) has been proposed by the medical community to manage the storage and distribution of images to radiologists, physicians, specialists, clinics, and imaging centers. There are three fundamental steps for Content Based Image Retrieval. They are Visual Feature Extraction, Similarity M...